Complex Care Management Program Overview

This report includes brief updates on various forms of complex care management including: Aetna - Medicare Advantage Embedded Case Management ProgramBrigham and Women's Hospital - Care Management ProgramIndependent Health - Care PartnersIntermountain Healthcare and Oregon Health and Science University - Care Management PlusJohns Hopkins University - Hospital at HomeMount Sinai Medical Center -- New York - Mount Sinai Visiting Doctors Program/ Chelsea-Village House Calls ProgramsPartners in Care Foundation - HomeMeds ProgramPrinceton HealthCare System - Partnerships for PIECEQuality Improvement for Complex Chronic Conditions - CarePartner ProgramSenior Services - Project Enhance/EnhanceWellnessSenior Whole Health - Complex Care Management ProgramSumma Health/Ohio Department of Aging - PASSPORT Medicaid Waiver ProgramSutter Health - Sutter Care Coordination ProgramUniversity of Washington School of Medicine - TEAMcar

Correlation of the Boost Risk Stratification Tool as a Predictor of Unplanned 30-Day Readmission in Elderly Patients

Carol K. Sieck Loyola University Chicago CORRELATION OF THE BOOST RISK STRATIFICATION TOOL AS A PREDICTOR OF UNPLANNED 30-DAY REAMDISSION IN ELDERLY PATIENTS Risk stratification tools can identify patients at risk for 30-day readmission but available tools lack predictive strength. While physical, functional and social determinants of health have demonstrated an association with readmission, available risk stratification tools have been inconsistent in their use of variables to predict readmission. The Better Outcomes by Optimizing Safe Transitions (BOOST) 8 P\u27s tool is a risk stratification tool developed by the Society of Hospital Medicine but has no published validation studies. The theoretical foundation used for this study was Wagner\u27s Care Model that illustrates the interconnected nature of acute and preventive care needed by chronically ill patients over a lifetime. This quantitative study using secondary data to measure the degree to which the BOOST variables predict 30-day readmission. The sample included one year of hospitalized patients 65+ (n=6849) from a tertiary hospital in the Midwest. Univariate and multivariate logistic regression demonstrated that six of the eight variables in the BOOST risk stratification tool showed significant predictive strength, including the social variables of health literacy (p=.030), depression (p=.003) and isolation (p=.011). Other significant variables included problem medications (p=.001), physical limitations (p=\u3c.001) and prior hospitalization (p=\u3c.001). The BOOST risk stratification tool had limited predictive capability with a C-statistic of .631. This study was the first attempt to validate the BOOST 8 P\u27s tool and to utilize nursing documentation within an electronic medical record to capture social determinants of health. Implications for nursing practice include the need for nurses to gain skills in using risk stratification tools to identify patients at risk for readmission to target preventive interventions including care coordination efforts. Future research should target variables, especially social factors of depression, health literacy and isolation to predict 30-day readmission, especially for the growing population of elderly patients with chronic illness

Predictive validity of the CriSTAL tool for short-term mortality in older people presenting at Emergency Departments: a prospective study

© 2018, The Author(s). Abstract: To determine the validity of the Australian clinical prediction tool Criteria for Screening and Triaging to Appropriate aLternative care (CRISTAL) based on objective clinical criteria to accurately identify risk of death within 3 months of admission among older patients. Methods: Prospective study of ≥ 65 year-olds presenting at emergency departments in five Australian (Aus) and four Danish (DK) hospitals. Logistic regression analysis was used to model factors for death prediction; Sensitivity, specificity, area under the ROC curve and calibration with bootstrapping techniques were used to describe predictive accuracy. Results: 2493 patients, with median age 78–80 years (DK–Aus). The deceased had significantly higher mean CriSTAL with Australian mean of 8.1 (95% CI 7.7–8.6 vs. 5.8 95% CI 5.6–5.9) and Danish mean 7.1 (95% CI 6.6–7.5 vs. 5.5 95% CI 5.4–5.6). The model with Fried Frailty score was optimal for the Australian cohort but prediction with the Clinical Frailty Scale (CFS) was also good (AUROC 0.825 and 0.81, respectively). Values for the Danish cohort were AUROC 0.764 with Fried and 0.794 using CFS. The most significant independent predictors of short-term death in both cohorts were advanced malignancy, frailty, male gender and advanced age. CriSTAL’s accuracy was only modest for in-hospital death prediction in either setting. Conclusions: The modified CriSTAL tool (with CFS instead of Fried’s frailty instrument) has good discriminant power to improve prognostic certainty of short-term mortality for ED physicians in both health systems. This shows promise in enhancing clinician’s confidence in initiating earlier end-of-life discussions

Assessing Prevalence of Known Risk Factors in a Regional Central Kentucky Medical Center Heart Failure Population as an Approach to Assessment of Needs for Development of a Program to Provide Targeted Services to Reduce 30 Day Readmissions

Abstract Objectives: Determine demographic, physiologic, and laboratory characteristics at time of admission of the heart failure (HF) population in a regional acute care facility in Central Kentucky through review of patient electronic medical records. Determine which HF population characteristics are significantly associated with readmissions to the hospital. Provide identification of the statistically significant common characteristics of the HF population to this facility so that they may work towards development of an electronic risk for readmission predictive instrument. Design: Retrospective chart review. Setting: Regional acute care facility in Central Kentucky. Participants: All patients (n = 175) with a diagnosis or history of HF (to include diagnosis related group (DRG) codes 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, 428.1, 428.41, 428.23, 428.43, 428.31, 428.33, 428.1, 428.20, 428.22, 428.30, 428.32, 428.40, 428.40, 428.42, 428.0, and 428.9; The Joint Commission, 2013) admitted to the acute care setting of a regional hospital in the Central Kentucky area between the dates of January 1, 2013 and July 31, 2013. Eligible participants were identified via an electronic discharge report listing all patients discharged during the study time period with a HF code. Main Outcome Measure: A chart review was performed to define the HF population within the regional acute care facility. Abstracted information was collected on data instruments (Appendices A,B, and C) and analyzed to define the overall HF population (n = 175). The data was then analyzed to determine significance between patient characteristics (demographic, physiologic, and laboratory) and 30 day readmissions. The data was examined both on the individual patient level and independent of patient level looking at each admission independently. Results: An in depth description of the HF patient population in this facility was obtained. Several patient characteristics including a history of anemia, COPD, ischemic heart disease, diabetes, and the laboratory values creatinine and BNP outside of the reference range were found to have a significant association with 30 day readmissions. Discharge to a skilled nursing facility (SNF) was also found to be a significant predictor of 30 day readmissions. Some social variables such as marital status were not found to have a significant relationship to 30 day readmissions. Conclusion: This investigation is a stepping stone to creating an electronic tool designed to reflect the characteristics of HF population admitted to a single facility and predict risk of HF readmissions within 30 days at the time of admission. Implementation of a plan of care designed to meet the needs of this HF population as well as identify those patients at high risk for will allow for provision of a comprehensive and timely individualized plan of care to reduce the incidence of 30 day readmissions

The Impact of Intensive Case Management on Hospice Utilization

Objective: The purpose of this study is to examine if patients enrolled in multi-disciplinary intensive case management program (ImPACT) alter the patient’s end-of-life path or setting of death. Methods: The quality improvement project is a quantitative retrospective study that compared patients receiving standard primary care vs intensive case management (ImPACT) during 2/2013-1/2014. It is a secondary analysis of a larger study of a quality improvement evaluation that took place at the Veterans Administration facility in Palo Alto, Ca. Results: Among the 82 patients who died, 19 were enrolled in ImPACT for approximately 249 days compared to 63 who received standard care. The patients had more than 10 chronic conditions with the average age of 71 years. There was a statistically significant relationship between the ImPACT patients and hospice utilization. 74 % of the ImPACT patients enrolled in hospice care vs 45% of the standard care group. There was no significant relationship between the days on hospice between both groups. However, the majority of the ImPACT patients died on inpatient VA hospice (50%) or home (26%) compared to standard care in which 27% died on inpatient VA hospice and 34% died at home. Conclusions: This study was the first to examine if intensive case management (ImPACT) would alter the patient’s end-of-life path or setting of death. ImPACT was successful in promoting hospice referral compared to patients receiving standard care

Comparing the predictive ability of the Revised Minimum Dataset Mortality Risk Index (MMRI-R) with nurses' predictions of mortality among frail older people: a cohort study.

OBJECTIVES: to establish the accuracy of community nurses' predictions of mortality among older people with multiple long-term conditions, to compare these with a mortality rating index and to assess the incremental value of nurses' predictions to the prognostic tool. DESIGN: a prospective cohort study using questionnaires to gather clinical information about patients case managed by community nurses. Nurses estimated likelihood of mortality for each patient on a 5-point rating scale. The dataset was randomly split into derivation and validation cohorts. Cox proportional hazard models were used to estimate risk equations for the Revised Minimum Dataset Mortality Risk Index (MMRI-R) and nurses' predictions of mortality individually and combined. Measures of discrimination and calibration were calculated and compared within the validation cohort. SETTING: two NHS Trusts in England providing case-management services by nurses for frail older people with multiple long-term conditions. PARTICIPANTS: 867 patients on the caseload of 35 case-management nurses. 433 and 434 patients were assigned to the derivation and validation cohorts, respectively. Patients were followed up for 12 months. RESULTS: 249 patients died (28.72%). In the validation cohort, MMRI-R demonstrated good discrimination (Harrell's c-index 0.71) and nurses' predictions similar discrimination (Harrell's c-index 0.70). There was no evidence of superiority in performance of either method individually (P = 0.83) but the MMRI-R and nurses' predictions together were superior to nurses' predictions alone (P = 0.01). CONCLUSIONS: patient mortality is associated with higher MMRI-R scores and nurses' predictions of 12-month mortality. The MMRI-R enhanced nurses' predictions and may improve nurses' confidence in initiating anticipatory care interventions

Intensive Care Admissions: Predicting Palliative Care Needs in the First 24 Hours

PURPOSE: The purpose of this retrospective analysis was to determine the proportion of intensive care admissions that required palliative care services during the same admission assessed by an investigator-developed palliative care screening tool. This study also analyzed the screening tool for the number of criteria producing the highest sensitivity and specificity for a palliative care consult occurring during the same hospital stay. METHODS: Retrospective data collection and analysis were performed by randomly selecting 110 patients records from a report obtained through the electronic health record, Epic. The sample was drawn from patients admitted to a medicine intensive care unit (2A) and neurology/neurosurgical intensive care unit (2B) at Baptist Health in Lexington Kentucky, a community-based tertiary care hospital, between April and August 2017. RESULTS: Screening tool items capturing more than one trigger point produced the highest sensitivity and specificity under a ROC curve (.7/.422) resulting in a palliative care consultation during the same hospital stay. The utilization of palliative consultations when criteria on the tool was triggered was low at 20/79 (25.3%) patients. A palliative consult, when indicated, was carried out a median of 5.5 days after the initial admission to the intensive care unit. Missed opportunities for palliative consults were discovered with 8 out of the remaining 59 patients who warranted, but did not receive a consult, died since the reviewed ICU admission. CONCLUSION: Palliative care consultations within the first twenty-four hours of an intensive care admission are needed but carried out at a low rate. The investigator-developed screening tool was effective in identifying the need for palliative care consultation. Palliative care screening tools need further validity testing as no standardize tool currently exists. Customizing tools for individual facility use is recommended and additional criteria should be considered

Accuracy of clinical predictions of prognosis at the end-of-life: evidence from routinely collected data in urgent care records

BACKGROUND: The accuracy of prognostication has important implications for patients, families, and health services since it may be linked to clinical decision-making, patient experience and outcomes and resource allocation. Study aim is to evaluate the accuracy of temporal predictions of survival in patients with cancer, dementia, heart, or respiratory disease. METHODS: Accuracy of clinical prediction was evaluated using retrospective, observational cohort study of 98,187 individuals with a Coordinate My Care record, the Electronic Palliative Care Coordination System serving London, 2010-2020. The survival times of patients were summarised using median and interquartile ranges. Kaplan Meier survival curves were created to describe and compare survival across prognostic categories and disease trajectories. The extent of agreement between estimated and actual prognosis was quantified using linear weighted Kappa statistic. RESULTS: Overall, 3% were predicted to live "days"; 13% "weeks"; 28% "months"; and 56% "year/years". The agreement between estimated and actual prognosis using linear weighted Kappa statistic was highest for patients with dementia/frailty (0.75) and cancer (0.73). Clinicians' estimates were able to discriminate (log-rank p < 0.001) between groups of patients with differing survival prospects. Across all disease groups, the accuracy of survival estimates was high for patients who were likely to live for fewer than 14 days (74% accuracy) or for more than one year (83% accuracy), but less accurate at predicting survival of "weeks" or "months" (32% accuracy). CONCLUSION: Clinicians are good at identifying individuals who will die imminently and those who will live for much longer. The accuracy of prognostication for these time frames differs across major disease categories, but remains acceptable even in non-cancer patients, including patients with dementia. Advance Care Planning and timely access to palliative care based on individual patient needs may be beneficial for those where there is significant prognostic uncertainty; those who are neither imminently dying nor expected to live for "years"

A Propensity-Matched, Mortality-Adjusted Study of Palliative Care Consult to Reduce 90-day Hospital Readmission in a Heart Failure Cohort

Centers for Medicare and Medicaid Services (CMS) instituted the Hospital Readmission Reduction Program (HRRP) to reduce the frequency of heart failure (HF) 30-day hospital readmissions. To meet the needs of patients with end-stage HF, palliative care (PC) is promoted to provide additional support to patients and reduceunnecessary hospital readmission. While PC is a plausible and logical intervention, effectiveness in achieving reductions in readmissions has not been assessed ina HF population with adequate controls to assess confounding. The goal of this research was to assess the effectiveness of Palliative care for HF (HFPC) consult to effect change in 90-day hospital readmissions in apropensity-matched model that adequately controls for mortality at a single-site 526-bed tertiary-care facility. Index hospitalization for live HF discharges: Oct 1 - Dec. 31, 2019, n= 250. Propensity matching helped to achieve a more homogeneous population with less variability ensuring a greater likelihood of observing an accurate and valid assessment of the outcome of interest. Results were statistically significant for a strong association between HFPC consult and 90-day hospital readmission in a propensity-matched population. Logistic regression found a statistically significant association between HFPC and 90-d hospital readmission, p\u3c .001. The logit transformation of the HFPC factor, OR 4.3, 95% CI [1.8 - 10.6] . Survival analysis demonstrates that time to readmission happens more frequently in patients who have a HFPC consult, readmissions occur earlier inthe post-discharge period and are strongly skewed to the immediate 30d post-discharge period. \u3e50% of HF patients who have a HFPC consult experience a hospitalreadmission within 30 days of discharge. \u3e75% of HF patients who have a HFPC consult will have a hospital readmission within 90 days of discharge. This study demonstrates that while HFPC may be an important aspect of continuity of care and care planning for HF patients, it has a strong negative association with the objective of reducing hospital readmissions. HFPC consult predicts earlier hospital re-admissions in this HF population with high morbidity, approaching end-of-life